Optimizing the Lifetime of Software Defined Wireless Sensor Network via Reinforcement Learning

IEEE Access

Muhammad Khurram Khan¹  Sharjeel Afridi²  Abdul Aleem Jamali³  Muhammad Rizwan Anjum⁴  Zulfiqar Ali Arain⁵  Muhammad Usman Younus⁶ 

[1] Center of Excellence in Information Assurance, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia;Department of Electrical Engineering, Sukkur IBA University, Sukkur, Pakistan;Department of Electronic Engineering, Quaid-e-Awam University of Engineering, Science and Technology (QUEST), Nawabshah, Pakistan;Department of Electronic Engineering, The Islamia University of Bahawalpur, Bahawalpur, Pakistan;Department of Telecommunication Engineering, Mehran University of Engineering and Technology, Jamshoro, Pakistan;Ecole Doctorale Mathéematiques, Informatique, Telecommunications de Toulouse, Paul Sabatier University, Toulouse, France;

关键词: Reinforcement learning;    wireless sensor network;    SDWSN;    RL-based WSN;    energy optimization;    routing;   

DOI  :  10.1109/ACCESS.2020.3046693

来源: DOAJ

【 摘 要 】

Reinforcement learning (RL) is an unsupervised learning technique used in many real-time applications. The essence of RL is a decision-making problem. In RL, the agent constantly interacts with the environment and selects the next action according to previous feedback in terms of reward. In this paper, RL trains Software-Defined Wireless Sensor Networks (SDWSNs) controller to optimize the routing paths. We combine RL and SDN, where RL is applied to the SDN controller to generate the routing tables. We also propose four different reward functions for optimization of the network performance. RL-based SDWSN improves network performance by 23% to 30% in terms of lifetime compared with RL-based routing techniques. RL-based SDWSN performs well because it can intelligently learn the routing path at the controller. In addition, it has a faster network convergence rate than RL-based WSN.

【 授权许可】

Unknown